{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2: Infiltration of Water into a Two-Layered Soil Profile "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook presents steps to replicate example 2 from: *David Rassam, Jirka Šimůnek, Dirk Mallants,and Martinus Th. van Genuchten, The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media* \\\n",
    "Tutorial \\\n",
    "Version 1.00, July 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example provides insctructions to create a Pydrus model that involves a time-variable atmospheric boundary condition with a soil profile consisting of a 50-cm soil layer of clay loam underlain by a 50-cm soil layer of sandy loam. The soil profile is initially unsaturated, having an initial pressure head of -100 cm. The upper boundary and lower boundary are represented with: \n",
    "\n",
    "* Upper BC: Atmospheric Boundary Condition with a Surface Layer (allows accumulation or ponding of water on soil surface).\n",
    "* Bottom BC: Seepage face boundary condition (water starts draining when the bottom of profile reaches full saturation). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the Pydrus package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pydrus as ps\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create the basic model & add time information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory example_2 created\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2, 4, 5, 6, 10, 20]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Folder for Hydrus files to be stored\n",
    "ws = \"example_2\"\n",
    "# Path to folder containing hydrus.exe \n",
    "exe = os.path.join(os.getcwd(),\"../../source/hydrus.exe\")  \n",
    "# Description\n",
    "desc = \"Infiltration of Water into a Two-Layered Soil Profile\"\n",
    "# Create model\n",
    "ml = ps.Model(exe_name=exe, ws_name=ws, name=\"model\", description=desc, \n",
    "              mass_units=\"mmol\", time_unit=\"days\", length_unit=\"cm\")\n",
    "ml.basic_info[\"lFlux\"] = True\n",
    "ml.basic_info[\"lShort\"] = False\n",
    "\n",
    "time = [2, 4, 5, 6, 10, 20]\n",
    "ml.add_time_info(tmax=20, print_array=time, dt=0.001, dtmax=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Add processes and materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>thr</th>\n",
       "      <th>ths</th>\n",
       "      <th>Alfa</th>\n",
       "      <th>n</th>\n",
       "      <th>Ks</th>\n",
       "      <th>l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.095</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1.31</td>\n",
       "      <td>6.24</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.065</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.075</td>\n",
       "      <td>1.89</td>\n",
       "      <td>106.10</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   water                                \n",
       "     thr   ths   Alfa     n      Ks    l\n",
       "1  0.095  0.41  0.019  1.31    6.24  0.5\n",
       "2  0.065  0.41  0.075  1.89  106.10  0.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.add_waterflow(model=3, top_bc=2, bot_bc=6)\n",
    "\n",
    "m = ml.get_empty_material_df(n=2)\n",
    "m.loc[[1, 2]] = [[0.095, 0.41, 0.019, 1.31, 6.24, 0.5],\n",
    "                 [0.065, 0.41, 0.075, 1.89, 106.1, 0.5]]\n",
    "ml.add_material(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Add profile information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = 100  # Disctretize soil column into n elements\n",
    "depth = [-51, -100]  # Depth of the soil column\n",
    "ihead = -100  # Determine initial Pressure Head\n",
    "# Create Profile\n",
    "profile = ps.create_profile(bot=depth, dx=abs(depth[-1] / nodes), h=ihead, mat=m.index)\n",
    "ml.add_profile(profile)  # Add the profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Add observation nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add observation nodes at depth\n",
    "ml.add_obs_nodes([0, -50, -100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Add atmosphere boundary conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = (2, 5, 7, 10, 20)\n",
    "bc = {\"tAtm\": time, \"Prec\": (6, 10, 2, 0, 0), \"rSoil\": (0, 0, 0, 0, 1)}\n",
    "atm = pd.DataFrame(bc, index=time)\n",
    "ml.add_atmospheric_bc(atm, hcrits=3, hcrita=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Write hydrus input files and run hydrus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['C:\\\\Matevz_arbeit\\\\pydrus\\\\examples\\\\hydrus_orig\\\\../../source/hydrus.exe', 'example_2', '-1'], returncode=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.write_input()\n",
    "ml.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-100, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "dfs = ml.read_obs_node()\n",
    "fig, ax = plt.subplots(figsize=(4,3))\n",
    "for i, df in dfs.items():\n",
    "    name = \"Node {}\".format(i)\n",
    "    df.plot(y=\"h\", ax=ax, label=name, use_index=True)\n",
    "ax.set_xlabel(\"Time [{}]\".format(ml.basic_info[\"TUnit\"]))\n",
    "ax.set_ylabel(\"h\")\n",
    "ax.set_ylim(-100, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29f9c26c940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(times=[2.0, 4.0, 5.0, 10.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29fa8243c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(\"Water Content\", times=[2.0, 10.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000029FA8302E48>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000029FA831D278>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(data=\"Actual Surface Flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000029FA8389438>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000029FA83B4240>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(data=\"Bottom Flux\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
